Design of an Adaptive Neural Network Controller for Robot Manipulators

Jing Ma; Xiao Ming Ji; Hong Yu Wu

doi:10.4028/www.scientific.net/AMR.787.876

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 787Design of an Adaptive Neural Network Controller...

Design of an Adaptive Neural Network Controller for Robot Manipulators

Abstract:

This paper brings forward a kind of adaptive neural-sliding model control schemes for uncertain robot trajectory tracking. The first scheme consists of a PD feedback and a dynamic compensator which is composed of RBF neural network and variable structure. The adaptive laws of Network weights are based on Lyapunov function method. This controller can guarantee stability of closed-loop system and asymptotic convergence of tracking errors.

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Periodical:

Advanced Materials Research (Volume 787)

Pages:

876-880

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.787.876

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Online since:

September 2013

Authors:

Jing Ma, Xiao Ming Ji, Hong Yu Wu

Keywords:

Adaptive Control, Neural Network (NN), Robotic Manipulators, Sliding-Model

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References

[1] Amor Jnifene, William Andrews. Experimental study on active vibration control of a single-Link flexible manipulator using tools of fuzzy logic and neural networks [J] , IEEE Transactions on Instrumentation and Measurement, 2005, 54(3): 1200-1208.

DOI: 10.1109/tim.2005.847136

Google Scholar

[2] Wang Hongrui, Li Erchao, Wei Lixing. Based on the adaptive control of X-Y positioning stage to improve the uncertainty of the neural network [J], Machine Tool & Hydraulics, 2006. 6: 187-189.

Google Scholar

[3] Huang S J, Shieh M H. Application of DSP Controller on X-Y Table Servo Control[J] , International Journey Advance Manufacture Technology, 2000, 16: 205- 211.

DOI: 10.1007/s001700050028

Google Scholar

[4] Farzaneh Abdollahi H.A. Talebi and Rajnikant V. Patel. A Stable Neural Network-Based Observer With Application to Flexible-Joint Manipulators[J], IEEE Trans on Neural Networks, 2006, 17(1): 118-129.

DOI: 10.1109/tnn.2005.863458

Google Scholar

[5] Wei Lixing, Tian Xuejing, Wang Hongrui. Based on the X-Y table sliding mode control of adaptive fuzzy control [J] , Electric Drive. 2009, 39(11): 56-59.

Google Scholar

[6] A. Green and J.Z. Sasiadek. Adaptive Control of a Flexible Robot Using Fuzzy Logic[C], AIAA. America institute of Aeronautics and Astronautics. 2004: 1-10.

DOI: 10.2514/6.2004-5117

Google Scholar

[7] Wen Shuhuan, Qiang Yanhui. Based on X-Y positioning force / position control of the modified Elman network [J], 2009, 37(7): 117-121.

Google Scholar

[8] Wang Zhonghua, Wang Xingsong, Xu Weiliang. Robust adaptive friction compensation of the X-Y position table [J], Southeast University, 2002, 32 (1): 69-72.

Google Scholar

[9] Yoshida K, Hashizume K, Abiko S. Zero reaction maneuver flight validation with ETS-VII space robot and extension to kinematically redundant arm[C]. Proceedings of the IEEE International conference on Robotics and Automation, Piscataway, USA: IEEE, 2001: 441-446.

DOI: 10.1109/robot.2001.932590

Google Scholar